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Machine Learning Guided Advanced Image Reconstruction in Photo Magnetic Imaging.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning Guided Advanced Image Reconstruction in Photo Magnetic Imaging./
Author:	Saraswatula, Janaki Sankirthana.
Description:	1 online resource (73 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-10.
Contained By:	Masters Abstracts International84-10.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30313215click for full text (PQDT)
ISBN:	9798379421021

Machine Learning Guided Advanced Image Reconstruction in Photo Magnetic Imaging.
Saraswatula, Janaki Sankirthana.

Machine Learning Guided Advanced Image Reconstruction in Photo Magnetic Imaging. - 1 online resource (73 pages)

Source: Masters Abstracts International, Volume: 84-10.

Thesis (M.S.)--University of California, Irvine, 2023.

Includes bibliographical references

Photo Magnetic Imaging (PMI) is a novel laser-based optical imaging technique. PMI is used to identify diseased tissue based on its endogenous tissue contrast or using disease-targeting exogenous optical contrast agents. It was first developed at the Center for Onco Functional Imaging at the University of California, Irvine. Optical imaging techniques generally suffer from poor spatial resolution due to the high scattering of optical photons in tissue. PMI attempts to provide quantitatively accurate optical images with high spatial resolution. In this study, phantoms mimicking optical properties of tissue are utilized to explore the use of Machine learning techniques and Finite Element Methods to drive an AI-based reconstruction of PMI images. Our proposed methodology consists of hybrid deep learning and a machine learning pipeline that first identifies the inclusions in the phantoms representing cancerous lesions and finally delineates their boundaries. Our method was tested on a variety of inclusions' sizes, locations, and absorption coefficients and demonstrated high precision (above 95%) and Intersection Over Union Accuracies (above 85%) for the realizable test cases.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379421021Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Deep learningIndex Terms--Genre/Form:

542853
Electronic books.

Machine Learning Guided Advanced Image Reconstruction in Photo Magnetic Imaging.
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500 $a Source: Masters Abstracts International, Volume: 84-10.
500 $a Advisor: Gulsen, Gultekin.
502 $a Thesis (M.S.)--University of California, Irvine, 2023.
504 $a Includes bibliographical references
520 $a Photo Magnetic Imaging (PMI) is a novel laser-based optical imaging technique. PMI is used to identify diseased tissue based on its endogenous tissue contrast or using disease-targeting exogenous optical contrast agents. It was first developed at the Center for Onco Functional Imaging at the University of California, Irvine. Optical imaging techniques generally suffer from poor spatial resolution due to the high scattering of optical photons in tissue. PMI attempts to provide quantitatively accurate optical images with high spatial resolution. In this study, phantoms mimicking optical properties of tissue are utilized to explore the use of Machine learning techniques and Finite Element Methods to drive an AI-based reconstruction of PMI images. Our proposed methodology consists of hybrid deep learning and a machine learning pipeline that first identifies the inclusions in the phantoms representing cancerous lesions and finally delineates their boundaries. Our method was tested on a variety of inclusions' sizes, locations, and absorption coefficients and demonstrated high precision (above 95%) and Intersection Over Union Accuracies (above 85%) for the realizable test cases.
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538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 649834
650 4 $a Computer engineering. $3 621879
650 4 $a Optics. $3 517925
653 $a Deep learning
653 $a Image processing
653 $a Machine learning
653 $a Medical imaging
653 $a Photo magnetic imaging
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773 0 $t Masters Abstracts International $g 84-10.
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